Poisson Kalman filter for disease surveillance

Donald Ebeigbe, Tyrus Berry, Steven J. Schiff, Timothy Sauer

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

An optimal filter for Poisson observations is developed as a variant of the traditional Kalman filter. Poisson distributions are characteristic of infectious diseases, which model the number of patients recorded as presenting each day to a health care system. We develop both a linear and a nonlinear (extended) filter. The methods are applied to a case study of neonatal sepsis and postinfectious hydrocephalus in Africa, using parameters estimated from publicly available data. Our approach is applicable to a broad range of disease dynamics, including both noncommunicable and the inherent nonlinearities of communicable infectious diseases and epidemics such as from COVID-19.

Original languageEnglish (US)
Article number043028
JournalPhysical Review Research
Volume2
Issue number4
DOIs
StatePublished - Oct 6 2020

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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